Update RAG_streamlit_app.py

RAG_streamlit_app.py

@@ -1,60 +1,61 @@
-import streamlit as st
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
-from sentence_transformers import SentenceTransformer
-import faiss, pickle
-import os
-from PIL import Image
-
-tokenizer = AutoTokenizer.from_pretrained("sentiment_model")
-model = AutoModelForSequenceClassification.from_pretrained("sentiment_model")
-clf = pipeline("text-classification", model=model, tokenizer=tokenizer, device=-1)
-embedder = SentenceTransformer("all-MiniLM-L6-v2", device="cpu")
-gen_tok = AutoTokenizer.from_pretrained("google/flan-t5-small")
-gen_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
-index = faiss.read_index("faiss_index.index")
-with open("passages.pkl", "rb") as f:
-    train_texts = pickle.load(f)
-
-def retrieve_passages(query, k=5):
-    q_emb = embedder.encode([query])
-    D, I = index.search(q_emb, k)
-    return [train_texts[i] for i in I[0]]
-
-def explain_sentiment(text, predicted_label, k=3):
-    retrieved = retrieve_passages(text, k=k)
-    prompt = f"""
-    Text to analyze: "{text}"
-    Predicted sentiment: {predicted_label}
-
-    Retrieved examples:
-    - {"\n- ".join(retrieved)}
-
-    Task: Write a clear explanation (1–2 sentences) about why the text is {predicted_label}. 
-    Focus on emotional words, tone, and context. Do NOT just repeat the label.
-    """
-    inputs = gen_tok(prompt, return_tensors="pt", truncation=True, max_length=512)
-    outputs = gen_model.generate(**inputs, max_length=80)
-    explanation = gen_tok.decode(outputs[0], skip_special_tokens=True)
-    return {"retrieved": retrieved, "explanation": explanation}
-
-st.title("Sentiment Analyzer")
-image = Image.open(r"Social-Sentiment-Tracking.png")
-st.image(image, width='stretch')
-text = st.text_area("Enter text to analyze:")
-if st.button("Predict"):
-    out = clf(text)[0]
-    pred = out['label']
-    score = out['score']
-    if pred == "LABEL_0":
-        human_pred = 'Negative'
-    elif pred == "LABEL_1":
-        human_pred = 'Neutral'
-    else:
-        human_pred = 'Positive'
-    st.write(f"**Prediction:** {human_pred} (confidence {score*100:.2f}%)")
-    rag_output = explain_sentiment(text, pred)
-    st.subheader("Retrieved evidence")
-    for r in rag_output['retrieved']:
-        st.write("-", r)
-    st.subheader("Explanation")
+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
+from sentence_transformers import SentenceTransformer
+import faiss, pickle
+import os
+from PIL import Image
+
+tokenizer = AutoTokenizer.from_pretrained("sentiment_model")
+model = AutoModelForSequenceClassification.from_pretrained("sentiment_model")
+clf = pipeline("text-classification", model=model, tokenizer=tokenizer, device=-1)
+embedder = SentenceTransformer("all-MiniLM-L6-v2", device="cpu")
+gen_tok = AutoTokenizer.from_pretrained("google/flan-t5-small")
+gen_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
+index = faiss.read_index("faiss_index.index")
+with open("passages.pkl", "rb") as f:
+    train_texts = pickle.load(f)
+
+def retrieve_passages(query, k=5):
+    q_emb = embedder.encode([query])
+    D, I = index.search(q_emb, k)
+    return [train_texts[i] for i in I[0]]
+
+def explain_sentiment(text, predicted_label, k=3):
+    retrieved = retrieve_passages(text, k=k)
+    retrieved_str = "\n- ".join(retrieved)
+    prompt = f"""
+    Text to analyze: "{text}"
+    Predicted sentiment: {predicted_label}
+
+    Retrieved examples:
+    - {retrieved_str}
+
+    Task: Write a clear explanation (1–2 sentences) about why the text is {predicted_label}. 
+    Focus on emotional words, tone, and context. Do NOT just repeat the label.
+    """
+    inputs = gen_tok(prompt, return_tensors="pt", truncation=True, max_length=512)
+    outputs = gen_model.generate(**inputs, max_length=80)
+    explanation = gen_tok.decode(outputs[0], skip_special_tokens=True)
+    return {"retrieved": retrieved, "explanation": explanation}
+
+st.title("Sentiment Analyzer")
+image = Image.open(r"Social-Sentiment-Tracking.png")
+st.image(image, width='stretch')
+text = st.text_area("Enter text to analyze:")
+if st.button("Predict"):
+    out = clf(text)[0]
+    pred = out['label']
+    score = out['score']
+    if pred == "LABEL_0":
+        human_pred = 'Negative'
+    elif pred == "LABEL_1":
+        human_pred = 'Neutral'
+    else:
+        human_pred = 'Positive'
+    st.write(f"**Prediction:** {human_pred} (confidence {score*100:.2f}%)")
+    rag_output = explain_sentiment(text, pred)
+    st.subheader("Retrieved evidence")
+    for r in rag_output['retrieved']:
+        st.write("-", r)
+    st.subheader("Explanation")
     st.write(rag_output['explanation'])